Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
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Improved Multidimensional Scaling Techniques Considering Cluster Analysis: Cluster-oriented Scaling

클러스터링을 고려한 다차원척도법의 개선: 군집 지향 척도법

  • Received : 2012.05.07
  • Accepted : 2012.05.30
  • Published : 2012.06.30
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Abstract

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

개체들 사이의 관계를 저차원 공간에 매핑하는 다차원척도법을 수행하기 위한 다양한 방법과 알고리즘이 개발되어왔다. 그러나 PROXSCAL이나 ALSCAL과 같은 기존의 기법들은 50개 이상의 개체를 포함하는 데이터 집합을 대상으로 개체 간의 관계와 군집 구조를 시각화하는데 있어서 효과적이지 못한 것으로 나타났다. 이 연구에서 제안하는 군집 지향 척도법 CLUSCAL(CLUster-oriented SCALing)은 기존 방법과 달리 입력되는 데이터의 군집 구조를 고려하도록 고안되었다. 50명의 저자동시인용 데이터와 85개 단어의 동시출현 데이터에 대해서 적용해본 결과 제안한 CLUSCAL 기법은 군집 구조를 잘 식별할 수 있는 MDS 지도를 생성하는 유용한 기법임이 확인되었다.

Keywords

References

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